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  <h1>Source code for torch.nn.modules.batchnorm</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">._functions</span> <span class="kn">import</span> <span class="n">SyncBatchNorm</span> <span class="k">as</span> <span class="n">sync_batch_norm</span>
<span class="kn">from</span> <span class="nn">.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">torch.nn.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">init</span>


<span class="k">class</span> <span class="nc">_NormBase</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Common base of _InstanceNorm and _BatchNorm&quot;&quot;&quot;</span>
    <span class="n">_version</span> <span class="o">=</span> <span class="mi">2</span>
    <span class="n">__constants__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;track_running_stats&#39;</span><span class="p">,</span> <span class="s1">&#39;momentum&#39;</span><span class="p">,</span> <span class="s1">&#39;eps&#39;</span><span class="p">,</span>
                     <span class="s1">&#39;num_features&#39;</span><span class="p">,</span> <span class="s1">&#39;affine&#39;</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                 <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_NormBase</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_features</span> <span class="o">=</span> <span class="n">num_features</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">affine</span> <span class="o">=</span> <span class="n">affine</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span> <span class="o">=</span> <span class="n">track_running_stats</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">affine</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">&#39;bias&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;running_mean&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;running_var&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;num_batches_tracked&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">&#39;running_mean&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">&#39;running_var&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">&#39;num_batches_tracked&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">reset_running_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">reset_running_stats</span><span class="p">()</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">affine</span><span class="p">:</span>
            <span class="n">init</span><span class="o">.</span><span class="n">ones_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
            <span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;</span><span class="si">{num_features}</span><span class="s1">, eps=</span><span class="si">{eps}</span><span class="s1">, momentum=</span><span class="si">{momentum}</span><span class="s1">, affine=</span><span class="si">{affine}</span><span class="s1">, &#39;</span> \
               <span class="s1">&#39;track_running_stats=</span><span class="si">{track_running_stats}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
                              <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">):</span>
        <span class="n">version</span> <span class="o">=</span> <span class="n">local_metadata</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;version&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span><span class="n">version</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">version</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="c1"># at version 2: added num_batches_tracked buffer</span>
            <span class="c1">#               this should have a default value of 0</span>
            <span class="n">num_batches_tracked_key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;num_batches_tracked&#39;</span>
            <span class="k">if</span> <span class="n">num_batches_tracked_key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
                <span class="n">state_dict</span><span class="p">[</span><span class="n">num_batches_tracked_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>

        <span class="nb">super</span><span class="p">(</span><span class="n">_NormBase</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_load_from_state_dict</span><span class="p">(</span>
            <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
            <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">_BatchNorm</span><span class="p">(</span><span class="n">_NormBase</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                 <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
            <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span> <span class="n">momentum</span><span class="p">,</span> <span class="n">affine</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_check_input_dim</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>

        <span class="c1"># exponential_average_factor is set to self.momentum</span>
        <span class="c1"># (when it is available) only so that it gets updated</span>
        <span class="c1"># in ONNX graph when this node is exported to ONNX.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="c1"># TODO: if statement only here to tell the jit to skip emitting this when it is None</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="o">+</span> <span class="mi">1</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># use cumulative moving average</span>
                    <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span><span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>  <span class="c1"># use exponential moving average</span>
                    <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span>

        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">(</span>
            <span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">,</span>
            <span class="n">exponential_average_factor</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>


<div class="viewcode-block" id="BatchNorm1d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.BatchNorm1d">[docs]</a><span class="k">class</span> <span class="nc">BatchNorm1d</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D</span>
<span class="sd">    inputs with optional additional channel dimension) as described in the paper</span>
<span class="sd">    `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension over</span>
<span class="sd">    the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set</span>
<span class="sd">    to 1 and the elements of :math:`\beta` are set to 0.</span>

<span class="sd">    Also by default, during training this layer keeps running estimates of its</span>
<span class="sd">    computed mean and variance, which are then used for normalization during</span>
<span class="sd">    evaluation. The running estimates are kept with a default :attr:`momentum`</span>
<span class="sd">    of 0.1.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``False``, this layer then does not</span>
<span class="sd">    keep running estimates, and batch statistics are instead used during</span>
<span class="sd">    evaluation time as well.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    Because the Batch Normalization is done over the `C` dimension, computing statistics</span>
<span class="sd">    on `(N, L)` slices, it&#39;s common terminology to call this Temporal Batch Normalization.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability.</span>
<span class="sd">            Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var</span>
<span class="sd">            computation. Can be set to ``None`` for cumulative moving average</span>
<span class="sd">            (i.e. simple average). Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters. Default: ``True``</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``True``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C)` or :math:`(N, C, L)`</span>
<span class="sd">        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm1d(100)</span>
<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm1d(100, affine=False)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`:</span>
<span class="sd">        https://arxiv.org/abs/1502.03167</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 2D or 3D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>


<div class="viewcode-block" id="BatchNorm2d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.BatchNorm2d">[docs]</a><span class="k">class</span> <span class="nc">BatchNorm2d</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs</span>
<span class="sd">    with additional channel dimension) as described in the paper</span>
<span class="sd">    `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension over</span>
<span class="sd">    the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set</span>
<span class="sd">    to 1 and the elements of :math:`\beta` are set to 0.</span>

<span class="sd">    Also by default, during training this layer keeps running estimates of its</span>
<span class="sd">    computed mean and variance, which are then used for normalization during</span>
<span class="sd">    evaluation. The running estimates are kept with a default :attr:`momentum`</span>
<span class="sd">    of 0.1.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``False``, this layer then does not</span>
<span class="sd">    keep running estimates, and batch statistics are instead used during</span>
<span class="sd">    evaluation time as well.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    Because the Batch Normalization is done over the `C` dimension, computing statistics</span>
<span class="sd">    on `(N, H, W)` slices, it&#39;s common terminology to call this Spatial Batch Normalization.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, H, W)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability.</span>
<span class="sd">            Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var</span>
<span class="sd">            computation. Can be set to ``None`` for cumulative moving average</span>
<span class="sd">            (i.e. simple average). Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters. Default: ``True``</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``True``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, H, W)`</span>
<span class="sd">        - Output: :math:`(N, C, H, W)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm2d(100)</span>
<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm2d(100, affine=False)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 35, 45)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`:</span>
<span class="sd">        https://arxiv.org/abs/1502.03167</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 4D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>


<div class="viewcode-block" id="BatchNorm3d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.BatchNorm3d">[docs]</a><span class="k">class</span> <span class="nc">BatchNorm3d</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs</span>
<span class="sd">    with additional channel dimension) as described in the paper</span>
<span class="sd">    `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension over</span>
<span class="sd">    the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set</span>
<span class="sd">    to 1 and the elements of :math:`\beta` are set to 0.</span>

<span class="sd">    Also by default, during training this layer keeps running estimates of its</span>
<span class="sd">    computed mean and variance, which are then used for normalization during</span>
<span class="sd">    evaluation. The running estimates are kept with a default :attr:`momentum`</span>
<span class="sd">    of 0.1.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``False``, this layer then does not</span>
<span class="sd">    keep running estimates, and batch statistics are instead used during</span>
<span class="sd">    evaluation time as well.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    Because the Batch Normalization is done over the `C` dimension, computing statistics</span>
<span class="sd">    on `(N, D, H, W)` slices, it&#39;s common terminology to call this Volumetric Batch Normalization</span>
<span class="sd">    or Spatio-temporal Batch Normalization.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, D, H, W)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability.</span>
<span class="sd">            Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var</span>
<span class="sd">            computation. Can be set to ``None`` for cumulative moving average</span>
<span class="sd">            (i.e. simple average). Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters. Default: ``True``</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``True``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, D, H, W)`</span>
<span class="sd">        - Output: :math:`(N, C, D, H, W)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm3d(100)</span>
<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm3d(100, affine=False)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 35, 45, 10)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`:</span>
<span class="sd">        https://arxiv.org/abs/1502.03167</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 5D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>


<div class="viewcode-block" id="SyncBatchNorm"><a class="viewcode-back" href="../../../../nn.html#torch.nn.SyncBatchNorm">[docs]</a><span class="k">class</span> <span class="nc">SyncBatchNorm</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs</span>
<span class="sd">    with additional channel dimension) as described in the paper</span>
<span class="sd">    `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension over all</span>
<span class="sd">    mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`</span>
<span class="sd">    are learnable parameter vectors of size `C` (where `C` is the input size).</span>
<span class="sd">    By default, the elements of :math:`\gamma` are sampled from</span>
<span class="sd">    :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.</span>

<span class="sd">    Also by default, during training this layer keeps running estimates of its</span>
<span class="sd">    computed mean and variance, which are then used for normalization during</span>
<span class="sd">    evaluation. The running estimates are kept with a default :attr:`momentum`</span>
<span class="sd">    of 0.1.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``False``, this layer then does not</span>
<span class="sd">    keep running estimates, and batch statistics are instead used during</span>
<span class="sd">    evaluation time as well.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    Because the Batch Normalization is done over the `C` dimension, computing statistics</span>
<span class="sd">    on `(N, +)` slices, it&#39;s common terminology to call this Volumetric Batch Normalization</span>
<span class="sd">    or Spatio-temporal Batch Normalization.</span>

<span class="sd">    Currently SyncBatchNorm only supports DistributedDataParallel with single GPU per process. Use</span>
<span class="sd">    torch.nn.SyncBatchNorm.convert_sync_batchnorm() to convert BatchNorm layer to SyncBatchNorm before wrapping</span>
<span class="sd">    Network with DDP.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, +)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability.</span>
<span class="sd">            Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var</span>
<span class="sd">            computation. Can be set to ``None`` for cumulative moving average</span>
<span class="sd">            (i.e. simple average). Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters. Default: ``True``</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``True``</span>
<span class="sd">        process_group: synchronization of stats happen within each process group</span>
<span class="sd">            individually. Default behavior is synchronization across the whole</span>
<span class="sd">            world</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, +)`</span>
<span class="sd">        - Output: :math:`(N, C, +)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.SyncBatchNorm(100)</span>
<span class="sd">        &gt;&gt;&gt; # creating process group (optional)</span>
<span class="sd">        &gt;&gt;&gt; # process_ids is a list of int identifying rank ids.</span>
<span class="sd">        &gt;&gt;&gt; process_group = torch.distributed.new_group(process_ids)</span>
<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.BatchNorm3d(100, affine=False, process_group=process_group)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 35, 45, 10)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">        &gt;&gt;&gt; # network is nn.BatchNorm layer</span>
<span class="sd">        &gt;&gt;&gt; sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)</span>
<span class="sd">        &gt;&gt;&gt; # only single gpu per process is currently supported</span>
<span class="sd">        &gt;&gt;&gt; ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(</span>
<span class="sd">        &gt;&gt;&gt;                         sync_bn_network,</span>
<span class="sd">        &gt;&gt;&gt;                         device_ids=[args.local_rank],</span>
<span class="sd">        &gt;&gt;&gt;                         output_device=args.local_rank)</span>

<span class="sd">    .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`:</span>
<span class="sd">        https://arxiv.org/abs/1502.03167</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                 <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">process_group</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">SyncBatchNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span> <span class="n">momentum</span><span class="p">,</span> <span class="n">affine</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">process_group</span> <span class="o">=</span> <span class="n">process_group</span>
        <span class="c1"># gpu_size is set through DistributedDataParallel initialization. This is to ensure that SyncBatchNorm is used</span>
        <span class="c1"># under supported condition (single GPU per process)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ddp_gpu_size</span> <span class="o">=</span> <span class="kc">None</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected at least 2D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>

    <span class="k">def</span> <span class="nf">_specify_ddp_gpu_num</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gpu_size</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">gpu_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;SyncBatchNorm is only supported for DDP with single GPU per process&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ddp_gpu_size</span> <span class="o">=</span> <span class="n">gpu_size</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="c1"># currently only GPU input is supported</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;SyncBatchNorm expected input tensor to be on GPU&#39;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_check_input_dim</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>

        <span class="c1"># exponential_average_factor is set to self.momentum</span>
        <span class="c1"># (when it is available) only so that it gets updated</span>
        <span class="c1"># in ONNX graph when this node is exported to ONNX.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="o">+</span> <span class="mi">1</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># use cumulative moving average</span>
                <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_batches_tracked</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="k">else</span><span class="p">:</span>  <span class="c1"># use exponential moving average</span>
                <span class="n">exponential_average_factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span>

        <span class="n">need_sync</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span>
        <span class="k">if</span> <span class="n">need_sync</span><span class="p">:</span>
            <span class="n">process_group</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_group</span><span class="p">:</span>
                <span class="n">process_group</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_group</span>
            <span class="n">world_size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">get_world_size</span><span class="p">(</span><span class="n">process_group</span><span class="p">)</span>
            <span class="n">need_sync</span> <span class="o">=</span> <span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span>

        <span class="c1"># fallback to framework BN when synchronization is not necessary</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">need_sync</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">(</span>
                <span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">,</span>
                <span class="n">exponential_average_factor</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">ddp_gpu_size</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s1">&#39;SyncBatchNorm is only supported within torch.nn.parallel.DistributedDataParallel&#39;</span><span class="p">)</span>

            <span class="k">return</span> <span class="n">sync_batch_norm</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
                <span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="p">,</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="n">exponential_average_factor</span><span class="p">,</span> <span class="n">process_group</span><span class="p">,</span> <span class="n">world_size</span><span class="p">)</span>

<div class="viewcode-block" id="SyncBatchNorm.convert_sync_batchnorm"><a class="viewcode-back" href="../../../../nn.html#torch.nn.SyncBatchNorm.convert_sync_batchnorm">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">convert_sync_batchnorm</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">module</span><span class="p">,</span> <span class="n">process_group</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Helper function to convert `torch.nn.BatchNormND` layer in the model to</span>
<span class="sd">        `torch.nn.SyncBatchNorm` layer.</span>

<span class="sd">        Args:</span>
<span class="sd">            module (nn.Module): containing module</span>
<span class="sd">            process_group (optional): process group to scope synchronization,</span>
<span class="sd">        default is the whole world</span>

<span class="sd">        Returns:</span>
<span class="sd">            The original module with the converted `torch.nn.SyncBatchNorm` layer</span>

<span class="sd">        Example::</span>

<span class="sd">            &gt;&gt;&gt; # Network with nn.BatchNorm layer</span>
<span class="sd">            &gt;&gt;&gt; module = torch.nn.Sequential(</span>
<span class="sd">            &gt;&gt;&gt;            torch.nn.Linear(20, 100),</span>
<span class="sd">            &gt;&gt;&gt;            torch.nn.BatchNorm1d(100)</span>
<span class="sd">            &gt;&gt;&gt;          ).cuda()</span>
<span class="sd">            &gt;&gt;&gt; # creating process group (optional)</span>
<span class="sd">            &gt;&gt;&gt; # process_ids is a list of int identifying rank ids.</span>
<span class="sd">            &gt;&gt;&gt; process_group = torch.distributed.new_group(process_ids)</span>
<span class="sd">            &gt;&gt;&gt; sync_bn_module = convert_sync_batchnorm(module, process_group)</span>

<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">module_output</span> <span class="o">=</span> <span class="n">module</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">modules</span><span class="o">.</span><span class="n">batchnorm</span><span class="o">.</span><span class="n">_BatchNorm</span><span class="p">):</span>
            <span class="n">module_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">SyncBatchNorm</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">num_features</span><span class="p">,</span>
                                                   <span class="n">module</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">momentum</span><span class="p">,</span>
                                                   <span class="n">module</span><span class="o">.</span><span class="n">affine</span><span class="p">,</span>
                                                   <span class="n">module</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">,</span>
                                                   <span class="n">process_group</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">module</span><span class="o">.</span><span class="n">affine</span><span class="p">:</span>
                <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
                    <span class="n">module_output</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
                    <span class="n">module_output</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
                <span class="c1"># keep requires_grad unchanged</span>
                <span class="n">module_output</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">requires_grad</span>
                <span class="n">module_output</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">requires_grad</span>
            <span class="n">module_output</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">running_mean</span>
            <span class="n">module_output</span><span class="o">.</span><span class="n">running_var</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">running_var</span>
            <span class="n">module_output</span><span class="o">.</span><span class="n">num_batches_tracked</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">num_batches_tracked</span>
        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
            <span class="n">module_output</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">convert_sync_batchnorm</span><span class="p">(</span><span class="n">child</span><span class="p">,</span> <span class="n">process_group</span><span class="p">))</span>
        <span class="k">del</span> <span class="n">module</span>
        <span class="k">return</span> <span class="n">module_output</span></div></div>
</pre></div>

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